{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:44:59Z","timestamp":1773215099249,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":99,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSF","award":["2112606"],"award-info":[{"award-number":["2112606"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,10]]},"DOI":"10.1145\/3514221.3517886","type":"proceedings-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T02:33:49Z","timestamp":1655001229000},"page":"247-261","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Interpretable Data-Based Explanations for Fairness Debugging"],"prefix":"10.1145","author":[{"given":"Romila","family":"Pradhan","sequence":"first","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"given":"Jiongli","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of California, San Diego, La Jolla, CA, USA"}]},{"given":"Boris","family":"Glavic","sequence":"additional","affiliation":[{"name":"Illinois Institute of Technology, Chicago, IL, USA"}]},{"given":"Babak","family":"Salimi","sequence":"additional","affiliation":[{"name":"University of California, San Diego, La Jolla, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,6,11]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"question and frisk data. https:\/\/www1.nyc.gov\/site\/nypd\/stats\/reports-analysis\/stopfrisk.page. [Online","author":"NYPD","year":"2021","unstructured":"NYPD stop, question and frisk data. https:\/\/www1.nyc.gov\/site\/nypd\/stats\/reports-analysis\/stopfrisk.page. [Online; accessed 19-October-2021]."},{"key":"e_1_3_2_2_2_1","volume-title":"https:\/\/www.nyclu.org\/en\/publications\/stop-and-frisk-de-blasio-era-2019. [Online","year":"2021","unstructured":"Stop-and-frisk in the de blasio era. https:\/\/www.nyclu.org\/en\/publications\/stop-and-frisk-de-blasio-era-2019. [Online; accessed 19-October-2021]."},{"key":"e_1_3_2_2_3_1","volume-title":"https:\/\/www.npr.org\/2019\/03\/28\/707614254\/hud-slaps-facebook-with-housing-discrimination-charge","author":"Housing","year":"2019","unstructured":"Housing department slaps facebook with discrimination charge. https:\/\/www.npr.org\/2019\/03\/28\/707614254\/hud-slaps-facebook-with-housing-discrimination-charge, 2019."},{"key":"e_1_3_2_2_4_1","volume-title":"https:\/\/www.technologyreview.com\/2019\/03\/01\/136808\/self-driving-cars-are-coming-but-accidents-may-not-be-evenly-distributed\/","year":"2019","unstructured":"Self-driving cars more likely to hit blacks. https:\/\/www.technologyreview.com\/2019\/03\/01\/136808\/self-driving-cars-are-coming-but-accidents-may-not-be-evenly-distributed\/, 2019."},{"key":"e_1_3_2_2_5_1","volume-title":"Explaining individual predictions when features are dependent: More accurate approximations to shapley values. arXiv preprint arXiv:1903.10464","author":"Aas Kjersti","year":"2019","unstructured":"Kjersti Aas, Martin Jullum, and Anders L\u00f8land. Explaining individual predictions when features are dependent: More accurate approximations to shapley values. arXiv preprint arXiv:1903.10464, 2019."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/645920.672836"},{"key":"e_1_3_2_2_7_1","volume-title":"Fairness debugging via interpretable data-based explanations. Technical report, https:\/\/drive.google.com\/drive\/folders\/1_jQnBg3JYus48HNyBh_QfZcd4wngFjBE?usp=sharing","year":"2021","unstructured":"anonymous. Fairness debugging via interpretable data-based explanations. Technical report, https:\/\/drive.google.com\/drive\/folders\/1_jQnBg3JYus48HNyBh_QfZcd4wngFjBE?usp=sharing, 2021."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00056"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457315"},{"key":"e_1_3_2_2_10_1","first-page":"7503","volume-title":"Proceedings of Machine Learning and Systems 2020","author":"Basu Samyadeep","year":"2020","unstructured":"Samyadeep Basu, Xuchen You, and Soheil Feizi. On second-order group influence functions for black-box predictions. In Proceedings of Machine Learning and Systems 2020, pages 7503--7512. 2020."},{"key":"e_1_3_2_2_11_1","first-page":"715","volume-title":"International Conference on Machine Learning","author":"Basu Samyadeep","year":"2020","unstructured":"Samyadeep Basu, Xuchen You, and Soheil Feizi. On second-order group influence functions for black-box predictions. In International Conference on Machine Learning, pages 715--724. PMLR, 2020."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3399579.3399865"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173951"},{"key":"e_1_3_2_2_14_1","volume-title":"Machine unlearning. arXiv preprint arXiv:1912.03817","author":"Bourtoule Lucas","year":"2019","unstructured":"Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. Machine unlearning. arXiv preprint arXiv:1912.03817, 2019."},{"key":"e_1_3_2_2_15_1","volume-title":"Exit through the training data: A look into instance-attribution explanations and efficient data deletion in machine learning. Area exam","author":"Brophy Jonathan","year":"2020","unstructured":"Jonathan Brophy. Exit through the training data: A look into instance-attribution explanations and efficient data deletion in machine learning. Area exam, University of Oregon, Computer and Information Sciences Department, 9 2020. Available at https:\/\/www.cs.uoregon.edu\/Reports\/AREA-202009-Brophy.pdf."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000050"},{"key":"e_1_3_2_2_17_1","first-page":"3992","volume-title":"Advances in Neural Information Processing Systems 30","author":"Calmon Flavio","year":"2017","unstructured":"Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney. Optimized pre-processing for discrimination prevention. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 3992--4001. Curran Associates, Inc., 2017."},{"key":"e_1_3_2_2_18_1","volume-title":"Fairness in machine learning: A survey. ArXiv, abs\/2010.04053","author":"Caton Simon","year":"2020","unstructured":"Simon Caton and C. Haas. Fairness in machine learning: A survey. ArXiv, abs\/2010.04053, 2020."},{"key":"e_1_3_2_2_19_1","volume-title":"Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526","author":"Chen Xinyun","year":"2017","unstructured":"Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song. Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526, 2017."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017801"},{"key":"e_1_3_2_2_21_1","volume-title":"An introduction to optimization","author":"Pin Chong Edwin Kah","year":"2013","unstructured":"Edwin Kah Pin Chong and Stanislaw H. Zak. An introduction to optimization. John Wiley & Sons, 2013."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2916074"},{"key":"e_1_3_2_2_23_1","first-page":"22","article-title":"Characterizations of an empirical influence function for detecting influential cases in regression","author":"Cook Dennis R.","year":"1980","unstructured":"Dennis R. Cook and Sanford Weisberg. Characterizations of an empirical influence function for detecting influential cases in regression. Technometrics, 22, 1980.","journal-title":"Technometrics"},{"key":"e_1_3_2_2_24_1","volume-title":"On the reasons behind decisions. arXiv preprint arXiv:2002.09284","author":"Darwiche Adnan","year":"2020","unstructured":"Adnan Darwiche and Auguste Hirth. On the reasons behind decisions. arXiv preprint arXiv:2002.09284, 2020."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.42"},{"key":"e_1_3_2_2_26_1","volume-title":"A guide to the california consumer privacy act of","author":"de la Torre Lydia","year":"2018","unstructured":"Lydia de la Torre. A guide to the california consumer privacy act of 2018. Available at SSRN 3275571, 2018."},{"key":"e_1_3_2_2_27_1","volume-title":"UCI machine learning repository","author":"Dua Dheeru","year":"2017","unstructured":"Dheeru Dua and Casey Graff. UCI machine learning repository, 2017."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411501.3419419"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/int.22415"},{"key":"e_1_3_2_2_31_1","volume-title":"Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability. arXiv preprint arXiv:1910.06358","author":"Frye Christopher","year":"2019","unstructured":"Christopher Frye, Ilya Feige, and Colin Rowat. Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability. arXiv preprint arXiv:1910.06358, 2019."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3681517"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3458455"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525269"},{"key":"e_1_3_2_2_35_1","series-title":"Proceedings of Machine Learning Research","first-page":"2242","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML","author":"Ghorbani Amirata","year":"2019","unstructured":"Amirata Ghorbani and James Y. Zou. Data shapley: Equitable valuation of data for machine learning. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9--15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 2242--2251. PMLR, 2019."},{"key":"e_1_3_2_2_36_1","volume-title":"Potential biases in machine learning algorithms using electronic health record data. JAMA internal medicine, 178(11):1544--1547","author":"Gianfrancesco Milena A","year":"2018","unstructured":"Milena A Gianfrancesco, Suzanne Tamang, Jinoos Yazdany, and Gabriela Schmajuk. Potential biases in machine learning algorithms using electronic health record data. JAMA internal medicine, 178(11):1544--1547, 2018."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-33-6546-9"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3452759"},{"key":"e_1_3_2_2_39_1","volume-title":"Adaptive machine unlearning. arXiv preprint arXiv:2106.04378","author":"Gupta Varun","year":"2021","unstructured":"Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. Adaptive machine unlearning. arXiv preprint arXiv:2106.04378, 2021."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335372"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/726"},{"key":"e_1_3_2_2_42_1","first-page":"2008","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Izzo Zachary","year":"2021","unstructured":"Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou. Approximate data deletion from machine learning models. In International Conference on Artificial Intelligence and Statistics, pages 2008--2016. PMLR, 2021."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445901"},{"key":"e_1_3_2_2_44_1","volume-title":"Niklas Pousette Harger, and Alina Oprea. Subpopulation data poisoning attacks. arXiv preprint arXiv:2006.14026","author":"Jagielski Matthew","year":"2020","unstructured":"Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, and Alina Oprea. Subpopulation data poisoning attacks. arXiv preprint arXiv:2006.14026, 2020."},{"key":"e_1_3_2_2_45_1","volume-title":"Niklas Pousette Harger, and Alina Oprea. Subpopulation data poisoning attacks. CoRR, abs\/2006.14026","author":"Jagielski Matthew","year":"2020","unstructured":"Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, and Alina Oprea. Subpopulation data poisoning attacks. CoRR, abs\/2006.14026, 2020."},{"key":"e_1_3_2_2_46_1","volume-title":"Model-agnostic counterfactual explanations for consequential decisions. arXiv preprint arXiv:1905.11190","author":"Karimi Amir-Hossein","year":"2019","unstructured":"Amir-Hossein Karimi, Gilles Barthe, Borja Belle, and Isabel Valera. Model-agnostic counterfactual explanations for consequential decisions. arXiv preprint arXiv:1905.11190, 2019."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445866"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702520"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1080\/1369118X.2018.1477967"},{"key":"e_1_3_2_2_50_1","first-page":"656","volume-title":"Advances in Neural Information Processing Systems","author":"Kilbertus Niki","year":"2017","unstructured":"Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Sch\u00f6lkopf. Avoiding discrimination through causal reasoning. In Advances in Neural Information Processing Systems, pages 656--666, 2017."},{"key":"e_1_3_2_2_51_1","volume-title":"International Convention Centre","author":"Koh Pang Wei","year":"2017","unstructured":"Pang Wei Koh and Percy Liang. Understanding black-box predictions via influence functions. volume 70 of Proceedings of Machine Learning Research, pages 1885--1894, International Convention Centre, Sydney, Australia, 06--11 Aug 2017. PMLR."},{"key":"e_1_3_2_2_52_1","first-page":"1885","volume-title":"International Conference on Machine Learning","author":"Koh Pang Wei","year":"2017","unstructured":"Pang Wei Koh and Percy Liang. Understanding black-box predictions via influence functions. In International Conference on Machine Learning, pages 1885--1894. PMLR, 2017."},{"key":"e_1_3_2_2_53_1","volume-title":"Stronger data poisoning attacks break data sanitization defenses. arXiv","author":"Koh Pang Wei","year":"2018","unstructured":"Pang Wei Koh, Jacob Steinhardt, and Percy Liang. Stronger data poisoning attacks break data sanitization defenses. arXiv 2018."},{"key":"e_1_3_2_2_54_1","first-page":"4069","volume-title":"Advances in Neural Information Processing Systems","author":"Kusner Matt J","year":"2017","unstructured":"Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. Counterfactual fairness. In Advances in Neural Information Processing Systems, pages 4069--4079, 2017."},{"key":"e_1_3_2_2_55_1","series-title":"Proceedings of Machine Learning Research","first-page":"793","volume-title":"Proceedings of The 24th International Conference on Artificial Intelligence and Statistics","author":"Kwon Yongchan","year":"2021","unstructured":"Yongchan Kwon, Manuel A. Rivas, and James Zou. Efficient computation and analysis of distributional shapley values. In Arindam Banerjee and Kenji Fukumizu, editors, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, pages 793--801. PMLR, 13--15 Apr 2021."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13347-017-0279-x"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1002\/asmb.446"},{"key":"e_1_3_2_2_58_1","volume-title":"Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning, DEEM'19","author":"Lourencco Raoni","year":"2019","unstructured":"Raoni Lourencco, Juliana Freire, and Dennis Shasha. Debugging machine learning pipelines. In Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning, DEEM'19, New York, NY, USA, 2019. Association for Computing Machinery."},{"key":"e_1_3_2_2_59_1","volume-title":"Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888","author":"Lundberg Scott M","year":"2018","unstructured":"Scott M Lundberg, Gabriel G Erion, and Su-In Lee. Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888, 2018."},{"key":"e_1_3_2_2_60_1","first-page":"4765","volume-title":"Advances in neural information processing systems","author":"Lundberg Scott M","year":"2017","unstructured":"Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Advances in neural information processing systems, pages 4765--4774, 2017."},{"key":"e_1_3_2_2_61_1","volume-title":"Preserving causal constraints in counterfactual explanations for machine learning classifiers. arXiv preprint arXiv:1912.03277","author":"Mahajan Divyat","year":"2019","unstructured":"Divyat Mahajan, Chenhao Tan, and Amit Sharma. Preserving causal constraints in counterfactual explanations for machine learning classifiers. arXiv preprint arXiv:1912.03277, 2019."},{"key":"e_1_3_2_2_62_1","volume-title":"Fairness and missing values. arXiv preprint arXiv:1905.12728","author":"Mart'inez-Plumed Fernando","year":"2019","unstructured":"Fernando Mart'inez-Plumed, C\u00e8sar Ferri, David Nieves, and Jos\u00e9 Hern\u00e1ndez-Orallo. Fairness and missing values. arXiv preprint arXiv:1905.12728, 2019."},{"key":"e_1_3_2_2_63_1","volume-title":"A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635","author":"Mehrabi Ninareh","year":"2019","unstructured":"Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635, 2019."},{"key":"e_1_3_2_2_64_1","volume-title":"Exacerbating algorithmic bias through fairness attacks. To appear in Proceedings of AAAI","author":"Mehrabi Ninareh","year":"2021","unstructured":"Ninareh Mehrabi, Muhammad Naveed, Fred Morstatter, and Aram Galstyan. Exacerbating algorithmic bias through fairness attacks. To appear in Proceedings of AAAI 2021."},{"key":"e_1_3_2_2_65_1","volume-title":"Exacerbating algorithmic bias through fairness attacks. arXiv preprint arXiv:2012.08723","author":"Mehrabi Ninareh","year":"2020","unstructured":"Ninareh Mehrabi, Muhammad Naveed, Fred Morstatter, and Aram Galstyan. Exacerbating algorithmic bias through fairness attacks. arXiv preprint arXiv:2012.08723, 2020."},{"key":"e_1_3_2_2_66_1","volume-title":"The explanation game: Explaining machine learning models with cooperative game theory. arXiv preprint arXiv:1909.08128","author":"Merrick Luke","year":"2019","unstructured":"Luke Merrick and Ankur Taly. The explanation game: Explaining machine learning models with cooperative game theory. arXiv preprint arXiv:1909.08128, 2019."},{"key":"e_1_3_2_2_67_1","volume-title":"Interpretable Machine Learning. Lulu. com","author":"Molnar Christoph","year":"2020","unstructured":"Christoph Molnar. Interpretable Machine Learning. Lulu. com, 2020."},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372850"},{"key":"e_1_3_2_2_69_1","first-page":"1931","volume-title":"Proceedings of the... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"2018","author":"Nabi Razieh","unstructured":"Razieh Nabi and Ilya Shpitser. Fair inference on outcomes. In Proceedings of the... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, volume 2018, page 1931. NIH Public Access, 2018."},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.18058"},{"key":"e_1_3_2_2_71_1","first-page":"8024","volume-title":"Advances in Neural Information Processing Systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. dtextquotesingle Alch\u00e9-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024--8035. Curran Associates, Inc., 2019."},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_2_73_1","volume-title":"Proceedings of the IEEE Int' Conf. on Data Engineering (ICDE), 2019","author":"Polyzotis Neoklis","year":"2019","unstructured":"Neoklis Polyzotis, Steven Whang, Tim Klas Kraska, and Yeounoh Chung. Slice finder: Automated data slicing for model validation. In Proceedings of the IEEE Int' Conf. on Data Engineering (ICDE), 2019, 2019."},{"key":"e_1_3_2_2_74_1","volume-title":"Robust fairness under covariate shift. arXiv preprint arXiv:2010.05166","author":"Rezaei Ashkan","year":"2020","unstructured":"Ashkan Rezaei, Anqi Liu, Omid Memarrast, and Brian Ziebart. Robust fairness under covariate shift. arXiv preprint arXiv:2010.05166, 2020."},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"e_1_3_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457323"},{"key":"e_1_3_2_2_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196914"},{"key":"e_1_3_2_2_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3319901"},{"key":"e_1_3_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457239"},{"key":"e_1_3_2_2_81_1","volume-title":"A symbolic approach to explaining bayesian network classifiers. arXiv preprint arXiv:1805.03364","author":"Shih Andy","year":"2018","unstructured":"Andy Shih, Arthur Choi, and Adnan Darwiche. A symbolic approach to explaining bayesian network classifiers. arXiv preprint arXiv:1805.03364, 2018."},{"key":"e_1_3_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445865"},{"key":"e_1_3_2_2_83_1","volume-title":"Poisoning attacks on algorithmic fairness. arXiv preprint arXiv:2004.07401","author":"Solans David","year":"2020","unstructured":"David Solans, Battista Biggio, and Carlos Castillo. Poisoning attacks on algorithmic fairness. arXiv preprint arXiv:2004.07401, 2020."},{"key":"e_1_3_2_2_84_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295110"},{"issue":"3474","key":"e_1_3_2_2_85_1","first-page":"3488","article-title":"Responsible data management","volume":"13","author":"Stoyanovich Julia","year":"2020","unstructured":"Julia Stoyanovich, Bill Howe, and H. V. Jagadish. Responsible data management. Proceedings of the VLDB Endowment, 13:3474 -- 3488, 2020.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_3_2_2_86_1","volume-title":"Explaining prediction models and individual predictions with feature contributions. Knowledge and information systems, 41(3):647--665","author":"Igor Kononenko Erik","year":"2014","unstructured":"Erik vS trumbelj and Igor Kononenko. Explaining prediction models and individual predictions with feature contributions. Knowledge and information systems, 41(3):647--665, 2014."},{"key":"e_1_3_2_2_87_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2017.29"},{"key":"e_1_3_2_2_88_1","unstructured":"https:\/\/docs.fast.ai\/tabular.learner.htm. Fastai neural network."},{"key":"e_1_3_2_2_89_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287566"},{"key":"e_1_3_2_2_90_1","volume-title":"Counterfactual explanations for machine learning: A review. arXiv preprint arXiv:2010.10596","author":"Verma Sahil","year":"2020","unstructured":"Sahil Verma, John Dickerson, and Keegan Hines. Counterfactual explanations for machine learning: A review. arXiv preprint arXiv:2010.10596, 2020."},{"key":"e_1_3_2_2_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/3194770.3194776"},{"key":"e_1_3_2_2_92_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"e_1_3_2_2_93_1","first-page":"841","article-title":"Counterfactual explanations without opening the black box: Automated decisions and the gdpr","volume":"31","author":"Wachter Sandra","year":"2017","unstructured":"Sandra Wachter, Brent Mittelstadt, and Chris Russell. Counterfactual explanations without opening the black box: Automated decisions and the gdpr. Harv. JL & Tech., 31:841, 2017.","journal-title":"Harv. JL & Tech."},{"key":"e_1_3_2_2_94_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445915"},{"key":"e_1_3_2_2_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389696"},{"key":"e_1_3_2_2_96_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3380571"},{"key":"e_1_3_2_2_97_1","volume-title":"Fairness-aware instrumentation of preprocessing pipelines for machine learning","author":"Yang K.","year":"2020","unstructured":"K. Yang, Biao Huang, Julia Stoyanovich, and Sebastian Schelter. Fairness-aware instrumentation of preprocessing pipelines for machine learning. 2020."},{"key":"e_1_3_2_2_98_1","volume-title":"Ian EH Yen, and Pradeep Ravikumar. Representer point selection for explaining deep neural networks. arXiv preprint arXiv:1811.09720","author":"Yeh Chih-Kuan","year":"2018","unstructured":"Chih-Kuan Yeh, Joon Sik Kim, Ian EH Yen, and Pradeep Ravikumar. Representer point selection for explaining deep neural networks. arXiv preprint arXiv:1811.09720, 2018."},{"key":"e_1_3_2_2_99_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11610"}],"event":{"name":"SIGMOD\/PODS '22: International Conference on Management of Data","location":"Philadelphia PA USA","acronym":"SIGMOD\/PODS '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 2022 International Conference on Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514221.3517886","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514221.3517886","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514221.3517886","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:36Z","timestamp":1750188636000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514221.3517886"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,10]]},"references-count":99,"alternative-id":["10.1145\/3514221.3517886","10.1145\/3514221"],"URL":"https:\/\/doi.org\/10.1145\/3514221.3517886","relation":{},"subject":[],"published":{"date-parts":[[2022,6,10]]},"assertion":[{"value":"2022-06-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}